EM algorithms for ML factor analysis

By Donald B. Rubin, Dorothy T. Thayer. 1982

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Citations in the corpus

High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes (2019, methodology): “two-dimensional time series of drawing trajectories [13, 14].”
Divergence Triangle for Joint Training of Generator Model, Energy-Based Model, and Inferential Model (2018, methodology): “The generator model [15], [16], [17], [19], [20] is a generalization of the factor analysis model [32],”
Cooperative Training of Descriptor and Generator Networks (2016, methodology): “A classical example is factor analysis [7].”
Alternating Back-Propagation for Generator Network (2016, methodology): “The factor analysis model can be learned by the Rubin-Thayer EM algorithm (Rubin and Thayer, 1982; Dempster, Laird, and Rubin, 1977), where both the E-step and the M-step are based on multivariate linear regression.”
Connectionist multivariate density-estimation and its application to speech synthesis (2016, methodology): “In order to maximize the marginal likelihood of the parameters of a factor analyser, the EM algorithm is used (Rubin and Thayer, 1982) due to the presence of the unobserved factors.”
Joint Factor Analysis Versus Eigenchannels in Speaker Recognition (2007, background): “…recognition is discriminative rather than generative, and it is concerned with finding feature representations which are immune to channel variability rather than modeling this type of variability, the key algorithm in “nuisance attribute projection” is also formulated as an eigenvalue problem.”
Continuous latent variable models for dimensionality reduction and sequential data reconstruction (2001, methodology): “2 Factor analysis and principal component analysis For all the experiments we report, maximum likelihood factor analysis was performed using an EM algorithm (Rubin and Thayer, 1982) with random starting points.”
STATISTICAL ANALYSIS OF FACTOR MODELS OF HIGH DIMENSION (2012, methodology): “The maximum likelihood estimation can be implemented via the EM algorithm and is considered by [21].”
Large Dimensional Factor Analysis (2008, background): “Results concerning the use of factors in forecasting are discussed in Stock and Watson (2006), Banerjee et al.”
Certifiably Optimal Low Rank Factor Analysis (2016, methodology): “Maximum Likelihood for Factor Analysis: This approach (Bai and Li, 2012; Joreskog, 1967; Mardia et al., 1979; Roweis and Ghahramani, 1999; Rubin and Thayer, 1982) is another widely used method in FA and typically assumes that the data follows a multivariate Gaussian distribution.”
Learning with Mixtures of Trees (2000, methodology): “Then the probability of the hidden variable given evidence V = x V is obtained by applying Bayes' rule as follows: In particular, when we observe all but the choice variable, i.e., V ≡ V and x V ≡ x, we obtain the posterior probability distribution of z:”